
import unittest

import numpy as np

from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torchaudio,
    slow,
)

from .test_pipelines_common import ANY




@is_pipeline_test
class 					lowerCamelCase__				(							unittest.TestCase):




	'''simple docstring'''




	_A								=	MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
	_A								=	TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING







	def   _lowerCamelCase							(   self						:str	,  a						:Optional[Any]	,  a						:List[str]	,  a						:Optional[int]		)						->					Union[str, Any]:
								__UpperCamelCase      :  int       =   AudioClassificationPipeline(model=a	,  feature_extractor=a		)

								# test with a raw waveform
								__UpperCamelCase      :  Union[str, Any]       =   np.zeros((3_4_0_0_0,)		)
								__UpperCamelCase      :  Optional[Any]       =   np.zeros((1_4_0_0_0,)		)
								return audio_classifier, [audioa, audio]







	def   _lowerCamelCase							(   self						:Dict	,  a						:Dict	,  a						:str		)						->					Optional[int]:
								__UpperCamelCase  ,		__UpperCamelCase      :  List[Any]       =   examples
								__UpperCamelCase      :  Dict       =   audio_classifier(a		)
								# by default a model is initialized with num_labels=2
								self.assertEqual(
								    a	,  [
								        {"score": ANY(a		), "label": ANY(a		)},
								        {"score": ANY(a		), "label": ANY(a		)},
								    ]	,  )
								__UpperCamelCase      :  Any       =   audio_classifier(a	,  top_k=1		)
								self.assertEqual(
								    a	,  [
								        {"score": ANY(a		), "label": ANY(a		)},
								    ]	,  )

								self.run_torchaudio(a		)







	@require_torchaudio
	def   _lowerCamelCase							(   self						:Optional[int]	,  a						:Optional[Any]		)						->					Optional[int]:
								import datasets

								# test with a local file
								__UpperCamelCase      :  int       =   datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy"	,  "clean"	,  split="validation"		)
								__UpperCamelCase      :  Dict       =   dataset[0]["audio"]["array"]
								__UpperCamelCase      :  List[Any]       =   audio_classifier(a		)
								self.assertEqual(
								    a	,  [
								        {"score": ANY(a		), "label": ANY(a		)},
								        {"score": ANY(a		), "label": ANY(a		)},
								    ]	,  )







	@require_torch
	def   _lowerCamelCase							(   self						:Dict		)						->					int:
								__UpperCamelCase      :  Dict       =   "anton-l/wav2vec2-random-tiny-classifier"

								__UpperCamelCase      :  Union[str, Any]       =   pipeline("audio-classification"	,  model=a		)

								__UpperCamelCase      :  Dict       =   np.ones((8_0_0_0,)		)
								__UpperCamelCase      :  Any       =   audio_classifier(a	,  top_k=4		)

								__UpperCamelCase      :  Optional[int]       =   [
								    {"score": 0.0842, "label": "no"},
								    {"score": 0.0838, "label": "up"},
								    {"score": 0.0837, "label": "go"},
								    {"score": 0.0834, "label": "right"},
								]
								__UpperCamelCase      :  Union[str, Any]       =   [
								    {"score": 0.0845, "label": "stop"},
								    {"score": 0.0844, "label": "on"},
								    {"score": 0.0841, "label": "right"},
								    {"score": 0.0834, "label": "left"},
								]
								self.assertIn(nested_simplify(a	,  decimals=4		)	,  [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]		)

								__UpperCamelCase      :  Optional[int]       =   {"array": np.ones((8_0_0_0,)		), "sampling_rate": audio_classifier.feature_extractor.sampling_rate}
								__UpperCamelCase      :  str       =   audio_classifier(a	,  top_k=4		)
								self.assertIn(nested_simplify(a	,  decimals=4		)	,  [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]		)







	@require_torch
	@slow
	def   _lowerCamelCase							(   self						:List[str]		)						->					str:
								import datasets

								__UpperCamelCase      :  Any       =   "superb/wav2vec2-base-superb-ks"

								__UpperCamelCase      :  Union[str, Any]       =   pipeline("audio-classification"	,  model=a		)
								__UpperCamelCase      :  Dict       =   datasets.load_dataset("anton-l/superb_dummy"	,  "ks"	,  split="test"		)

								__UpperCamelCase      :  Any       =   np.array(dataset[3]["speech"]	,  dtype=np.floataa		)
								__UpperCamelCase      :  Any       =   audio_classifier(a	,  top_k=4		)
								self.assertEqual(
								    nested_simplify(a	,  decimals=3		)	,  [
								        {"score": 0.981, "label": "go"},
								        {"score": 0.007, "label": "up"},
								        {"score": 0.006, "label": "_unknown_"},
								        {"score": 0.001, "label": "down"},
								    ]	,  )







	@require_tf
	@unittest.skip("Audio classification is not implemented for TF"		)
	def   _lowerCamelCase							(   self						:List[str]		)						->					Optional[Any]:
								pass

import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Optional[int]		,    _lowerCamelCase					:     List[Any]		,    _lowerCamelCase					:     Optional[Any])   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  str       =   BertConfig.from_json_file(_lowerCamelCase)
							print(F'Building PyTorch model from configuration: {config}')
							__UpperCamelCase      :  Optional[Any]       =   BertForPreTraining(_lowerCamelCase)

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase)

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}')
							torch.save(model.state_dict()		,    _lowerCamelCase)


if __name__ == "__main__":
		lowercase    :   Union[str, Any]						=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained BERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		lowercase    :   Any						=			parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)